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Yearb Med Inform. 2019 Aug;28(1):128-134. doi: 10.1055/s-0039-1677903. Epub 2019 Apr 25.

Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications.

Author information

1
Macquarie University, Australian Institute of Health Innovation, Sydney, Australia.
2
UMIT, University for Health Sciences, Medical Informatics and Technology, Institute of Medical Informatics, Hall in Tyrol, Austria.
3
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
4
Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health research institute, The Netherlands.
5
National Institute for Health and Welfare, Information Department, Helsinki, Finland.
6
Tampere University, Faculty for Information Technology and Communication Sciences, Tampere, Finland.
7
Keele University, School of Social Science and Public Policy, Keele, United Kingdom.
8
University of Portsmouth, Centre for Healthcare Modelling and Informatics, Portsmouth, United Kingdom.
9
St. Luke's International University, Tokyo, Japan.

Abstract

OBJECTIVES:

This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance.

METHOD:

A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems.

RESULTS:

There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed.

CONCLUSION:

Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.

PMID:
31022752
PMCID:
PMC6697499
DOI:
10.1055/s-0039-1677903
[Indexed for MEDLINE]
Free PMC Article

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